Meeting report

1 Introduction

1.1 Documenting community changes

• Many papers report local species richness and temporal trends
  • Abundance (Klink et al. 2020)
  • Species richness: (Vellend et al. 2013; Dornelas et al. 2014)
• Other aspects of community structure vary through time:
  • Composition: (Dornelas et al. 2014; Hillebrand et al. 2018)
  • Functional diversity (Barnagaud et al. 2017)
  • Evenness (ref)

1.2 Why do we want to assess community changes ?

• Global changes have affect a tremendous effect communities
• Understanding assembly and disassembly rules
• Give a more complex and complete picture of what is going on ?

1.3 Link between different facets of biodiversity

• Species richness:
  • Evenness / richness: (+) (Soininen, Passy, and Hillebrand 2012)
  • FDiv / richness: (+) (Mouchet et al. 2010)
  • FEve / richness: (O) (Mouchet et al. 2010) … to complete

1.4 Research questions

2 Methods

2.1 Data selection

• Unique protocol and unique unit of abundance by site
• One sampling by year and site
• In each site, samplings should be in the median (more or less one month and half, i.e. 1.5 month)
• 10 samplings by site

loc <- filtered_dataset$location %>%
  left_join(filtered_dataset$site_quali, by = "siteid") %>%
  st_as_sf(coords = c("longitude", "latitude"),
  crs = 4326)

fig_sites <- paste0(
  "Australia absence because span is superior to 10 years of 100s of sites but number of samplings < 10.")

world <- ne_countries(scale = "medium", returnclass = "sf")
bb <- st_bbox(loc)
ggplot(data = world) +
    geom_sf() +
    geom_sf(data = loc, aes(color = protocol), shape = 1) +
    coord_sf(
      xlim = bb[c("xmin", "xmax")],
      ylim = bb[c("ymin", "ymax")],
      expand = FALSE
      ) +
    theme(legend.position = "bottom") +
    labs(title = paste0("Number of sites: ", nrow(loc)))

Figure 2.1: Australia absence because span is superior to 10 years of 100s of sites but number of samplings < 10.

2.2 Biodiversity

2.2.1 Quantity

• Hill numbers
  • \(H^0\): Species richness
  • \(H^1\): Shannon
  • \(H^2\): Simpson

We used also hill numbers with coverage-based correction proposed by (???)

• Evenness:
  • Pielou: \(J = H^1 / log(S)\)

2.2.2 Betadiversity

Betadiversity metrics compares the composition of two communities. In our case, we compare the composition of each time step relatively to the baseline, i.e. the first year of sampling.

• Species exchange index: (Hillebrand et al. 2018)
  • Presence-absence: \(J = SER_r = \dfrac{S_{imm} + S_{lost}}{S_{tot}}\)

  • Relative abundance: \(SER_a = \dfrac{\sum_i (p_i - p^{\prime}_i)^2}{\sum_i p_i^2 + \sum_i p^{\prime2}_i - \sum_i p_i p^{\prime}_i}\)

  • \(S_{imm}\), \(S_{lost}\), \(S_{tot}\): number of immigrant, lost and total species respectively
  • \(i\): species \(i\), \(p\): relative abundance, \(\prime\): second community

  • \(J\): Jaccard index

Turnover metrics based on presence/absence can be decomposed in appearance/disappearance and nestedness / turnover.

• appearance: \(\dfrac{S_{imm}}{S_{tot}}\), disappearance: \(\dfrac{S_{lost}}{S_{tot}}\)

• Turnover: \(J_t = \dfrac{2 * min(S_{lost}, S_{imm})}{S_{common} + (2 * min(S_{lost}, S_{imm})}\)

• Nestedness: \(SER_r - J_t\)

3 Environment (Coming soon)

4 Statistical analysis

• Simple lm by site

5 Results

5.1 Direction of trends

ti <- map_dfr(rigal_trends, ~tabyl_df(x = .x, group = "direction"),
  .id = "response"
)
ti %>%
  filter(direction != "Total") %>%
  select(response, direction, percent) %>%
  mutate(response = str_replace_all(response, get_var_replacement())) %>%
  pivot_wider(names_from = "direction", values_from = "percent") %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "bordered", "hover"))

response	stable	increase	decrease
Total abundance	75.8%	14.3%	9.9%
Log Total Turnover (jaccard) (codyn) abundance	73.3%	16.5%	10.2%
Species richness	77.7%	13.2%	7.4%
Log species richness	76.2%	13.2%	6.6%
Chao species richness	79.8%	10.6%	8.2%
Chao Shannon	76.5%	12.1%	7.7%
Chao Simpson	78.4%	10.5%	7.4%
Chao Evenness	82.8%	7.5%	5.9%
Jaccard (binary, similarity)	76.8%	2.8%	17.5%
Horn (binary, similarity)	77.3%	2.7%	17.2%
Chao (binary, similarity)	71.5%	2.3%	8.3%
SER_a (rel abundance)	75.3%	4.1%	18.6%
Total Turnover (jaccard) (codyn)	73.7%	18.3%	3.0%
Appearance	70.2%	15.6%	3.8%
disAppearance	64.0%	9.5%	3.0%
Evenness	78.8%	9.4%	8.1%
Shannon	75.7%	12.2%	8.3%
Simpson	77.9%	10.8%	7.7%
Jaccard (binary, dissimilarity)	74.7%	17.4%	2.8%
Nestedness (jaccard)	80.3%	9.3%	5.3%
Turnover (jaccard)	57.4%	8.3%	1.7%

ti <- map_dfr(rigal_trends, ~tabyl_df(x = .x, group = "shape_class"),
  .id = "response"
)

ti %>%
  filter(shape_class != "Total") %>%
  select(response, shape_class, percent) %>%
  mutate(response = str_replace_all(response, get_var_replacement())) %>%
  pivot_wider(names_from = "shape_class", values_from = "percent") %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "bordered", "hover"))

response	stable_constant	increase_constant	decrease_constant	stable_convex	stable_concave	decrease_decelerated	increase_accelerated	increase_decelerated	decrease_accelerated	NA_constant
Total abundance	62.1%	13.7%	8.7%	7.8%	5.9%	1.2%	0.5%	0.1%	0.0%	NA
Log Total Turnover (jaccard) (codyn) abundance	57.8%	14.4%	9.5%	6.6%	8.9%	0.3%	0.3%	1.8%	0.4%	NA
Species richness	63.7%	11.9%	6.8%	5.2%	8.8%	0.4%	0.5%	0.8%	0.2%	1.7%
Log species richness	62.9%	11.8%	6.1%	3.8%	9.5%	0.3%	0.3%	1.0%	0.2%	4.0%
Chao species richness	68.0%	9.5%	7.5%	5.5%	6.3%	0.5%	0.6%	0.6%	0.1%	1.4%
Chao Shannon	64.2%	10.8%	7.3%	4.8%	7.4%	0.3%	0.5%	0.7%	0.1%	3.8%
Chao Simpson	66.5%	9.4%	6.9%	4.9%	7.1%	0.4%	0.4%	0.6%	NA	3.8%
Chao Evenness	73.3%	7.1%	5.6%	4.1%	5.4%	0.1%	0.1%	0.3%	0.1%	3.8%
Jaccard (binary, similarity)	64.4%	2.7%	16.3%	7.1%	5.3%	0.7%	0.0%	0.1%	0.5%	2.9%
Horn (binary, similarity)	65.2%	2.5%	15.9%	6.4%	5.7%	0.5%	NA	0.2%	0.8%	2.8%
Chao (binary, similarity)	55.4%	1.9%	6.5%	8.7%	7.4%	0.7%	NA	0.4%	1.0%	17.9%
SER_a (rel abundance)	60.6%	3.8%	16.7%	7.9%	6.8%	1.1%	0.0%	0.3%	0.8%	2.0%
Total Turnover (jaccard) (codyn)	63.7%	17.2%	2.8%	4.8%	5.3%	0.1%	0.7%	0.4%	0.0%	4.9%
Appearance	60.9%	14.2%	3.7%	4.1%	5.3%	0.1%	0.8%	0.6%	NA	10.3%
disAppearance	54.7%	8.9%	2.7%	5.4%	4.0%	0.3%	0.4%	0.2%	NA	23.5%
Evenness	67.4%	8.6%	7.7%	5.4%	6.0%	0.3%	0.2%	0.6%	0.1%	3.7%
Shannon	63.1%	11.0%	7.8%	4.8%	7.9%	0.3%	0.4%	0.8%	0.2%	3.7%
Simpson	65.4%	9.5%	7.2%	4.7%	7.7%	0.2%	0.4%	0.9%	0.3%	3.7%
Jaccard (binary, dissimilarity)	63.6%	16.2%	2.6%	4.6%	6.5%	0.1%	0.5%	0.7%	0.0%	5.1%
Nestedness (jaccard)	70.8%	8.3%	5.0%	4.6%	4.9%	0.3%	0.6%	0.4%	0.0%	5.2%
Turnover (jaccard)	50.5%	8.0%	1.6%	3.5%	3.4%	0.1%	0.2%	0.1%	NA	32.6%

5.2 Average

slope <- map_dfr(rigal_trends,
  ~.x %>%
  select(siteid, linear_slope),
.id = "response"
)

rigal_trends_df <-  map2_dfr(
  rigal_trends, names(rigal_trends),
  ~.x %>% mutate(variable = .y)
  ) %>%
select(variable, siteid, linear_slope) %>%
pivot_wider(names_from = "variable", values_from = "linear_slope")

summary_slope <- slope %>%
  group_by(response) %>%
  summarise(summ = list(enframe(summary_distribution(linear_slope, na.rm = TRUE)))) %>%
  unnest(cols = summ) %>%
  pivot_wider(names_from = "name", values_from = "value") %>%
  select(response, mean, median, sd)
summary_slope %>%
  mutate(response = get_var_replacement()[response]) %>%
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "bordered", "hover")) %>%
  scroll_box(width = "100%", height = "1000px")

response	mean	median	sd
Appearance	0.0027393	0.0009691	0.0098864
Chao (binary, similarity)	-0.0029650	0.0000000	0.0138050
Chao evenness	0.0000118	0.0000000	0.0687473
Chao species richness	0.0073944	0.0002926	0.1387268
Chao shannon	0.0043759	0.0007389	0.0822483
Chao simpson	0.0028965	0.0007189	0.0691378
Disappearance	0.0017718	0.0000000	0.0071868
Evenness	0.0006221	0.0000000	0.0144629
SER_a (rel abundance)	-0.0072135	-0.0032189	0.0183244
Horn (binary, similarity)	-0.0041715	-0.0027316	0.0113065
Jaccard (binary, similarity)	-0.0051970	-0.0037510	0.0135836
Jaccard (binary, dissimilarity)	0.0051970	0.0037510	0.0135836
Log species richness	0.2947554	0.0498002	2.6535099
Log total abundance	0.8272072	0.4993106	6.6756922
Nestedness (jaccard)	0.0021108	0.0002881	0.0137269
Shannon	0.0016449	0.0004650	0.0221596
Simpson	0.0007642	0.0001071	0.0111222
Species richness	0.0148247	0.0020269	0.1422215
Total turnover (codyn)	0.0045111	0.0032257	0.0115567
Total abundance	1.7786740	0.1129827	31.5332673
Turnover (jaccard)	0.0030863	0.0000000	0.0134089

p_jt_sup_ne <- sum(abs(rigal_trends_df$turnover) > abs(rigal_trends_df$nestedness)) /
  nrow(rigal_trends_df)

• To compare with Dornelas et al. (2014):
  • Species richness:
    • Dornelas et al. (2014): 1557 measurements of species richness in two consecutive times, 629 (40%) increase, 624 (40%) decrease, and 304 (20%) do not change.
    • Our study: 13% increase, 7% decrease, 78% do not change, median: + 50% species by decade (i.e from 2 to 3 species, 10 to 15 species in 10 years)
  • Jaccard:
    • 10% species replaced per decade (Dornelas et al. 2014)
    • 28% of species replaced per decade (Blowes et al. 2019)
    • our study: 4% of species per decade
  • Turnover / nestedness:
    • Blowes et al. (2019): 97% of study shows turnover exceeding nestedness
    • our study: 43%
  • Jaccard vs dominance-based SER:
    • low correlation: \(0.34\)

rigal_trends_df %>%
  ggplot(aes(x = jaccard , y = hillebrand)) +
  geom_point() +
#  geom_smooth(method = "gam") +
  labs(x = "Jaccard trends (similarity, binary)", y = "Hillebrand trends
    (similarity, relative abundance)")

rigal_trends_df_loc <- filtered_dataset$location %>%
  left_join(rigal_trends_df, by = "siteid") %>%
  st_as_sf(coords = c("longitude", "latitude"),
  crs = 4326)

5.3 Relationships between temporal trends

knitr::include_graphics(here("doc", "fig", "p_cor_slope_tot.png"))

ti <- expand.grid(
  resp1 = unique(slope$response),
  resp2 = unique(slope$response)
  ) %>%
  filter(resp2 != resp1) %>%
  filter(
    resp1 %in% c("chao_richness", "species_nb", "log_species_nb",
      "total_abundance", "log_total_abundance")) %>%
  mutate_all(as.character) %>%
  arrange(resp1)

test <- map2(ti$resp1, ti$resp2,
  function(x, y) {
    bi <- slope %>%
      filter(response %in% c(x, y)) %>%
      select(siteid, response, linear_slope) %>%
      pivot_wider(names_from = "response", values_from = "linear_slope")

    return(bi)
  }
)

p_trends_trends <- map(test, function(x) {
  l <- colnames(x)
  x %>%
    ggplot(aes(x = !!sym(l[2]), y = !!sym(l[3]))) +
    geom_point() +
    geom_smooth(method = "loess") +
    labs(
      x = get_var_replacement()[l[2]],
      y = get_var_replacement()[l[3]]
    )
})
names(p_trends_trends) <- map_chr(test, ~colnames(.x)[2])

plot_grid(
  plotlist = p_trends_trends[names(p_trends_trends) %in% "log_species_nb"],
  ncol = 3
)
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'
#> `geom_smooth()` using formula 'y ~ x'

5.4 Spatial trends

5.4.1 Map

5.4.1.1 Site

p <- map(c("log_species_nb", "log_total_abundance", "jaccard", "hillebrand", "appearance", "disappearance", "turnover", "nestedness"),
 ~ map_world_trends(
   df = rigal_trends_df_loc, y = .x, range_per = .95) +
   labs(title = get_var_replacement()[.x])
)
p
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5.4.1.2 Median trends by Basin

basin_slope <- rigal_trends_df_loc %>%
  group_by(main_bas) %>%
  summarise(across(where(is.double), median))

p_basin <- map(c("log_species_nb", "log_total_abundance", "jaccard", "hillebrand", "appearance", "disappearance", "turnover", "nestedness"),
 ~ map_world_trends(
   df = basin_slope, y = .x, range_per = .95) +
   labs(title = get_var_replacement()[.x])
)
p_basin
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5.4.1.3 Country

country_slope <- rigal_trends_df_loc %>%
  group_by(country) %>%
  summarise(across(where(is.double), median))

p_country <- map(c("log_species_nb", "log_total_abundance", "jaccard", "hillebrand", "appearance", "disappearance", "turnover", "nestedness"),
 ~ map_world_trends(
   df = country_slope, y = .x, range_per = .95) +
   labs(title = get_var_replacement()[.x])
)
p_country
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5.4.2 Boxplot

b_country <- map(c("log_species_nb", "log_total_abundance", "jaccard", "hillebrand", "appearance", "disappearance", "turnover", "nestedness"),
 ~ rigal_trends_df_loc %>%
  ggplot(aes(x = country, y = !!sym(.x), fill = ecoregion)) +
  geom_boxplot() +
labs(title = get_var_replacement()[.x])
)
b_country
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5.5 Rarity

Cf other document

5.6 Spatial scale

• Selection on basin data

5.7 Analysis

5.8 Reproducibility

Reproducibility receipt

## datetime
Sys.time()
#> [1] "2022-01-20 10:49:39 CST"

## repository
if(requireNamespace('git2r', quietly = TRUE)) {
  git2r::repository()
} else {
  c(
    system2("git", args = c("log", "--name-status", "-1"), stdout = TRUE),
    system2("git", args = c("remote", "-v"), stdout = TRUE)
  )
}
#> Local:    main /home/alain/Documents/post-these/isu/RivFishTimeBiodiversityFacets
#> Head:     [3695d10] 2022-01-20: fix hillebrand: change entry matrix

## session info
sessionInfo()
#> R version 4.0.5 (2021-03-31)
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#> [79] readxl_1.3.1       data.table_1.13.6  git2r_0.29.0      
#> [82] callr_3.7.0        reprex_1.0.0       digest_0.6.27     
#> [85] classInt_0.4-3     webshot_0.5.2      munsell_0.5.0     
#> [88] bslib_0.2.4

References

Barnagaud, Jean-Yves, Pierre Gaüzère, Benjamin Zuckerberg, Karine Princé, and Jens-Christian Svenning. 2017. “Temporal Changes in Bird Functional Diversity Across the United States.” Oecologia 185 (4): 737–48. https://doi.org/10.1007/s00442-017-3967-4.

Blowes, Shane A., Sarah R. Supp, Laura H. Antão, Amanda Bates, Helge Bruelheide, Jonathan M. Chase, Faye Moyes, et al. 2019. “The Geography of Biodiversity Change in Marine and Terrestrial Assemblages.” Science 366 (6463): 339–45. https://doi.org/10.1126/science.aaw1620.

Dornelas, Maria, Nicholas J. Gotelli, Brian McGill, Hideyasu Shimadzu, Faye Moyes, Caya Sievers, and Anne E. Magurran. 2014. “Assemblage Time Series Reveal Biodiversity Change but Not Systematic Loss.” Science 344 (6181): 296–99. https://doi.org/10.1126/science.1248484.

Hillebrand, Helmut, Bernd Blasius, Elizabeth T. Borer, Jonathan M. Chase, John A. Downing, Britas Klemens Eriksson, Christopher T. Filstrup, et al. 2018. “Biodiversity Change Is Uncoupled from Species Richness Trends: Consequences for Conservation and Monitoring.” Journal of Applied Ecology 55 (1): 169–84. https://doi.org/https://doi.org/10.1111/1365-2664.12959.

Klink, Roel van, Diana E. Bowler, Konstantin B. Gongalsky, Ann B. Swengel, Alessandro Gentile, and Jonathan M. Chase. 2020. “Meta-Analysis Reveals Declines in Terrestrial but Increases in Freshwater Insect Abundances.” Science 368 (6489): 417–20. https://doi.org/10.1126/science.aax9931.

Mouchet, Maud A., Sébastien Villéger, Norman W. H. Mason, and David Mouillot. 2010. “Functional Diversity Measures: An Overview of Their Redundancy and Their Ability to Discriminate Community Assembly Rules.” Functional Ecology 24 (4): 867–76. https://doi.org/10.1111/j.1365-2435.2010.01695.x.

Soininen, Janne, Sophia Passy, and Helmut Hillebrand. 2012. “The Relationship Between Species Richness and Evenness: A Meta-Analysis of Studies Across Aquatic Ecosystems.” Oecologia 169 (3): 803–9. https://doi.org/10.1007/s00442-011-2236-1.

Vellend, Mark, Lander Baeten, Isla H. Myers-Smith, Sarah C. Elmendorf, Robin Beauséjour, Carissa D. Brown, Pieter De Frenne, Kris Verheyen, and Sonja Wipf. 2013. “Global Meta-Analysis Reveals No Net Change in Local-Scale Plant Biodiversity over Time.” Proceedings of the National Academy of Sciences 110 (48): 19456–9.